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| **[Roadmap](https://github.com/ai-dynamo/dynamo/issues/5506)** | **[Support Matrix](https://github.com/ai-dynamo/dynamo/blob/main/docs/reference/support-matrix.md)** | **[Docs](https://docs.nvidia.com/dynamo/latest/index.html)** | **[Recipes](https://github.com/ai-dynamo/dynamo/tree/main/recipes)** | **[Examples](https://github.com/ai-dynamo/dynamo/tree/main/examples)** | **[Prebuilt Containers](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/collections/ai-dynamo)** | **[Design Proposals](https://github.com/ai-dynamo/enhancements)** | **[Blogs](https://developer.nvidia.com/blog/tag/nvidia-dynamo)**
# NVIDIA Dynamo
High-throughput, low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments.
## Why Dynamo
<p align="center">
<img src="./docs/images/frontpage-gpu-vertical.png" alt="Multi Node Multi-GPU topology" width="600" />
</p>
Large language models exceed single-GPU capacity. Tensor parallelism spreads layers across GPUs but creates coordination challenges. Dynamo closes this orchestration gap.
Dynamo is inference engine agnostic (supports TRT-LLM, vLLM, SGLang) and provides:
- **Disaggregated Prefill & Decode** – Maximizes GPU throughput with latency/throughput trade-offs
- **Dynamic GPU Scheduling** – Optimizes performance based on fluctuating demand
- **LLM-Aware Request Routing** – Eliminates unnecessary KV cache re-computation
- **Accelerated Data Transfer** – Reduces inference response time using NIXL
- **KV Cache Offloading** – Leverages multiple memory hierarchies for higher throughput
Built in Rust for performance and Python for extensibility, Dynamo is fully open-source with an OSS-first development approach.
## Backend Feature Support
| | [SGLang](docs/backends/sglang/README.md) | [TensorRT-LLM](docs/backends/trtllm/README.md) | [vLLM](docs/backends/vllm/README.md) |
|---|:----:|:----------:|:--:|
| **Best For** | High-throughput serving | Maximum performance | Broadest feature coverage |
| [**Disaggregated Serving**](docs/design_docs/disagg_serving.md) | ✅ | ✅ | ✅ |
| [**KV-Aware Routing**](docs/router/kv_cache_routing.md) | ✅ | ✅ | ✅ |
| [**SLA-Based Planner**](docs/planner/sla_planner.md) | ✅ | ✅ | ✅ |
| [**KVBM**](docs/kvbm/kvbm_architecture.md) | 🚧 | ✅ | ✅ |
| [**Multimodal**](docs/multimodal/index.md) | ✅ | ✅ | ✅ |
| [**Tool Calling**](docs/agents/tool-calling.md) | ✅ | ✅ | ✅ |
> **[Full Feature Matrix →](docs/reference/feature-matrix.md)** — Detailed compatibility including LoRA, Request Migration, Speculative Decoding, and feature interactions.
## Dynamo Architecture
<p align="center">
<img src="./docs/images/frontpage-architecture.png" alt="Dynamo architecture" width="600" />
</p>
> **[Architecture Deep Dive →](docs/design_docs/architecture.md)**
## Latest News
- [12/05] [Moonshot AI's Kimi K2 achieves 10x inference speedup with Dynamo on GB200](https://quantumzeitgeist.com/kimi-k2-nvidia-ai-ai-breakthrough/)
- [12/02] [Mistral AI runs Mistral Large 3 with 10x faster inference using Dynamo](https://www.marktechpost.com/2025/12/02/nvidia-and-mistral-ai-bring-10x-faster-inference-for-the-mistral-3-family-on-gb200-nvl72-gpu-systems/)
- [12/01] [InfoQ: NVIDIA Dynamo simplifies Kubernetes deployment for LLM inference](https://www.infoq.com/news/2025/12/nvidia-dynamo-kubernetes/)
## Get Started
| Path | Use Case | Time | Requirements |
|------|----------|------|--------------|
| [**Local Quick Start**](#local-quick-start) | Test on a single machine | ~5 min | 1 GPU, Ubuntu 24.04 |
| [**Kubernetes Deployment**](#kubernetes-deployment) | Production multi-node clusters | ~30 min | K8s cluster with GPUs |
| [**Building from Source**](#building-from-source) | Contributors and development | ~15 min | Ubuntu, Rust, Python |
Want to help shape the future of distributed LLM inference? See the **[Contributing Guide](CONTRIBUTING.md)**.
# Local Quick Start
The following examples require a few system level packages.
Recommended to use Ubuntu 24.04 with a x86_64 CPU. See [docs/reference/support-matrix.md](docs/reference/support-matrix.md)
## Install Dynamo
### Option A: Containers (Recommended)
Containers have all dependencies pre-installed. No setup required.
```bash
# SGLang
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.9.0
# TensorRT-LLM
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:0.9.0
# vLLM
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.9.0
```
> **Tip:** To run frontend and worker in the same container, either run processes in background with `&` (see below), or open a second terminal and use `docker exec -it <container_id> bash`.
See [Release Artifacts](docs/reference/release-artifacts.md#container-images) for available versions.
### Option B: Install from PyPI
The Dynamo team recommends the `uv` Python package manager, although any way works.
```bash
# Install uv (recommended Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Create virtual environment
uv venv venv
source venv/bin/activate
uv pip install pip
```
Install system dependencies and the Dynamo wheel for your chosen backend:
**SGLang**
```bash
sudo apt install python3-dev
uv pip install "ai-dynamo[sglang]"
```
> **Note:** For CUDA 13 (B300/GB300), the container is recommended. See [SGLang install docs](https://docs.sglang.io/get_started/install.html) for details.
**TensorRT-LLM**
```bash
sudo apt install python3-dev
pip install torch==2.9.0 torchvision --index-url https://download.pytorch.org/whl/cu130
pip install --pre --extra-index-url https://pypi.nvidia.com "ai-dynamo[trtllm]"
```
> **Note:** TensorRT-LLM requires `pip` due to a transitive Git URL dependency that `uv` doesn't resolve. We recommend using the [TensorRT-LLM container](docs/reference/release-artifacts.md#container-images) for broader compatibility.
**vLLM**
```bash
sudo apt install python3-dev libxcb1
uv pip install "ai-dynamo[vllm]"
```
## Run Dynamo
> **Tip (Optional):** Before running Dynamo, verify your system configuration with `python3 deploy/sanity_check.py`
Dynamo provides a simple way to spin up a local set of inference components including:
- **OpenAI Compatible Frontend** – High performance OpenAI compatible http api server written in Rust.
- **Basic and Kv Aware Router** – Route and load balance traffic to a set of workers.
- **Workers** – Set of pre-configured LLM serving engines.
Start the frontend:
> **Tip:** To run in a single terminal (useful in containers), append `> logfile.log 2>&1 &` to run processes in background. Example: `python3 -m dynamo.frontend --store-kv file > dynamo.frontend.log 2>&1 &`
```bash
# Start an OpenAI compatible HTTP server with prompt templating, tokenization, and routing.
# For local dev: --store-kv file avoids etcd (workers and frontend must share a disk)
python3 -m dynamo.frontend --http-port 8000 --store-kv file
```
In another terminal (or same terminal if using background mode), start a worker for your chosen backend:
```bash
# SGLang
python3 -m dynamo.sglang --model-path Qwen/Qwen3-0.6B --store-kv file
# TensorRT-LLM
python3 -m dynamo.trtllm --model-path Qwen/Qwen3-0.6B --store-kv file
# vLLM (note: uses --model, not --model-path)
python3 -m dynamo.vllm --model Qwen/Qwen3-0.6B --store-kv file \
--kv-events-config '{"enable_kv_cache_events": false}'
```
> **Note:** For dependency-free local development, disable KV event publishing (avoids NATS):
> - **vLLM:** Add `--kv-events-config '{"enable_kv_cache_events": false}'`
> - **SGLang:** No flag needed (KV events disabled by default)
> - **TensorRT-LLM:** No flag needed (KV events disabled by default)
>
> **TensorRT-LLM only:** The warning `Cannot connect to ModelExpress server/transport error. Using direct download.` is expected and can be safely ignored.
>
> See [Service Discovery and Messaging](#service-discovery-and-messaging) for details.
#### Send a Request
```bash
curl localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"messages": [
{
"role": "user",
"content": "Hello, how are you?"
}
],
"stream":false,
"max_tokens": 300
}' | jq
```
Rerun with `curl -N` and change `stream` in the request to `true` to get the responses as soon as the engine issues them.
# Kubernetes Deployment
For production deployments on Kubernetes clusters with multiple GPUs.
## Prerequisites
- Kubernetes cluster with GPU nodes
- [Dynamo Platform installed](docs/kubernetes/README.md)
- HuggingFace token for model downloads
## Production Recipes
Pre-built deployment configurations for common models and topologies:
| Model | Framework | Mode | GPUs | Recipe |
|-------|-----------|------|------|--------|
| Llama-3-70B | vLLM | Aggregated | 4x H100 | [View](recipes/llama-3-70b/vllm/) |
| DeepSeek-R1 | SGLang | Disaggregated | 8x H200 | [View](recipes/deepseek-r1/sglang/) |
| Qwen3-32B-FP8 | TensorRT-LLM | Aggregated | 8x GPU | [View](recipes/qwen3-32b-fp8/trtllm/) |
See [recipes/README.md](recipes/README.md) for the full list and deployment instructions.
## Cloud Deployment Guides
- [Amazon EKS](examples/deployments/EKS/)
- [Google GKE](examples/deployments/GKE/)
# Building from Source
For contributors who want to build Dynamo from source rather than installing from PyPI.
## 1. Install Libraries
**Ubuntu:**
```
sudo apt install -y build-essential libhwloc-dev libudev-dev pkg-config libclang-dev protobuf-compiler python3-dev cmake
```
**macOS:**
- [Homebrew](https://brew.sh/)
```
# if brew is not installed on your system, install it
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
```
- [Xcode](https://developer.apple.com/xcode/)
```
brew install cmake protobuf
## Check that Metal is accessible
xcrun -sdk macosx metal
```
If Metal is accessible, you should see an error like `metal: error: no input files`, which confirms it is installed correctly.
## 2. Install Rust
```
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env
```
## 3. Create a Python Virtual Environment
Follow the instructions in [uv installation](https://docs.astral.sh/uv/#installation) guide to install uv if you don't have `uv` installed. Once uv is installed, create a virtual environment and activate it.
- Install uv
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
- Create a virtual environment
```bash
uv venv dynamo
source dynamo/bin/activate
```
## 4. Install Build Tools
```
uv pip install pip maturin
```
[Maturin](https://github.com/PyO3/maturin) is the Rust<->Python bindings build tool.
## 5. Build the Rust Bindings
```
cd lib/bindings/python
maturin develop --uv
```
## 6. Install GPU Memory Service
The GPU Memory Service is a Python package with a C++ extension. It requires only Python development headers and a C++ compiler (g++).
```bash
cd $PROJECT_ROOT
uv pip install -e lib/gpu_memory_service
```
## 7. Install the Wheel
```
cd $PROJECT_ROOT
uv pip install -e .
```
## 8. Run the Frontend
```bash
python3 -m dynamo.frontend
```
## 9. Configure for Local Development
- Pass `--store-kv file` to avoid external dependencies (see [Service Discovery and Messaging](#service-discovery-and-messaging))
- Set `DYN_LOG` to adjust the logging level (e.g., `export DYN_LOG=debug`). Uses the same syntax as `RUST_LOG`
> **Note:** VSCode and Cursor users can use the `.devcontainer` folder for a pre-configured dev environment. See the [devcontainer README](.devcontainer/README.md) for details.
# Advanced Topics
## Benchmarking
Dynamo provides comprehensive benchmarking tools:
- **[Benchmarking Guide](docs/benchmarks/benchmarking.md)** – Compare deployment topologies using AIPerf
- **[SLA-Driven Deployments](docs/planner/sla_planner_quickstart.md)** – Optimize deployments to meet SLA requirements
## Frontend OpenAPI Specification
The OpenAI-compatible frontend exposes an OpenAPI 3 spec at `/openapi.json`. To generate without running the server:
```bash
cargo run -p dynamo-llm --bin generate-frontend-openapi
```
This writes to `docs/frontends/openapi.json`.
## Service Discovery and Messaging
Dynamo uses TCP for inter-component communication. On Kubernetes, native resources ([CRDs + EndpointSlices](docs/kubernetes/service_discovery.md)) handle service discovery. External services are optional for most deployments:
| Deployment | etcd | NATS | Notes |
|------------|------|------|-------|
| **Local Development** | ❌ Not required | ❌ Not required | Pass `--store-kv file`; vLLM also needs `--kv-events-config '{"enable_kv_cache_events": false}'` |
| **Kubernetes** | ❌ Not required | ❌ Not required | K8s-native discovery; TCP request plane |
> **Note:** KV-Aware Routing requires NATS for prefix caching coordination.
For Slurm or other distributed deployments (and KV-aware routing):
- [etcd](https://etcd.io/) can be run directly as `./etcd`.
- [nats](https://nats.io/) needs JetStream enabled: `nats-server -js`.
To quickly setup both: `docker compose -f deploy/docker-compose.yml up -d`
See [SGLang on Slurm](examples/backends/sglang/slurm_jobs/README.md) and [TRT-LLM on Slurm](examples/basics/multinode/trtllm/README.md) for deployment examples.
## More News
- [11/20] [Dell integrates PowerScale with Dynamo's NIXL for 19x faster TTFT](https://www.dell.com/en-us/dt/corporate/newsroom/announcements/detailpage.press-releases~usa~2025~11~dell-technologies-and-nvidia-advance-enterprise-ai-innovation.htm)
- [11/20] [WEKA partners with NVIDIA on KV cache storage for Dynamo](https://siliconangle.com/2025/11/20/nvidia-weka-kv-cache-solution-ai-inferencing-sc25/)
- [11/13] [Dynamo Office Hours Playlist](https://www.youtube.com/playlist?list=PL5B692fm6--tgryKu94h2Zb7jTFM3Go4X)
- [10/16] [How Baseten achieved 2x faster inference with NVIDIA Dynamo](https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/)
<!-- Reference links for Feature Compatibility Matrix -->
[disagg]: docs/design_docs/disagg_serving.md
[kv-routing]: docs/router/kv_cache_routing.md
[planner]: docs/planner/sla_planner.md
[kvbm]: docs/kvbm/kvbm_architecture.md
[mm]: examples/multimodal/
[migration]: docs/fault_tolerance/request_migration.md
[lora]: examples/backends/vllm/deploy/lora/README.md
[tools]: docs/agents/tool-calling.md